$F0$ Estimation and Voicing Detection With Cascade Architecture in Noisy Speech

As a fundamental problem in speech processing, pitch tracking has been studied for decades. While strong performance has been achieved on clean speech, pitch tracking in noisy speech is still challenging. Severe non-stationary noises not only corrupt the harmonic structure in voiced intervals but also make it difficult to determine the existence of voiced speech. Given the importance of voicing detection for pitch tracking, this study proposes a neural cascade architecture that jointly performs pitch estimation and voicing detection. The cascade architecture optimizes a speech enhancement module and a pitch tracking module, and is trained in a speaker-independent and noise-independent way. It is observed that incorporating the enhancement module improves both pitch estimation and voicing detection accuracy, especially in low signal-to-noise ratio (SNR) conditions. In addition, compared with frameworks that combine corresponding single-task models, the proposed multi-task framework achieves better performance and is more efficient. Experimental results show that the proposed method is robust to different noise conditions and substantially outperforms other competitive pitch tracking methods.